基于随机森林算法的环焊缝质量不合格性分析预测

Analysis and Prediction of Girth Weld Defects Based on Random Forest Algorithm

油气长输管道环焊缝一直以来是管道的薄弱环节,容易发生失效。根据环焊缝开挖数据,对大量环焊缝相关数据进行了预处理,并采用机器学习中的随机森林方法对主要因素进行了分析,建立了环焊缝质量不合格性预测模型,并通过不同算法模型进行了验证。结果表明,构建的模型可以实现环焊缝质量不合格预测。对环焊缝的数据分析和机器学习模型的构建,可以提高环焊缝开挖准确率,节省开挖费用,为后续开挖和修复工作提供技术支持。

Girth weld of long-distance oil and gas pipeline has always been the weak link of the pipeline and is easy to fail.According to the girth weld excavation data,this paper preprocesses a large number of girth weld related data,analyzes the main factors by using random forest algorithm,constructs the unqualified girth weld quality prediction model,and verifies the model through different algorithm models.The results show that the model can predict the unqualified girth weld quality.Through the data analysis of girth weld and the construction of machine learning model,the accuracy of girth weld excavation can be improved,the cost of excavation can be saved,and the technical support can be provided for the subsequent excavation and repair work.